Paper Citation Record · LEDGER

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation

As of 13 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 2 inbound Pith citation observations for arXiv:2411.13836.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.13836 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:53:31.889301Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:04:44.106636Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-13T22:21:15.797283Z

Reference resolution

51 of 51 outbound references displayed

verified exact2
verified fuzzy42
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e8d4222c-c7a9-42dd-8f17-90c0bb26a74d · outbound

This paper cites Weakly su- pervised learning of instance segmentation with inter-pixel relations.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Weakly su- pervised learning of instance segmentation with inter-pixel relations

Reference 1

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Observation 6c6c5a6a-84a4-4045-aa37-4967a6e6d61d · outbound

This paper cites Zero-shot semantic segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Zero-shot semantic segmentation

Reference 2

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Observation d18a48e3-a586-4cd5-b360-d17f0e54e0a9 · outbound

This paper cites Coco- stuff: Thing and stuff classes in context.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Coco- stuff: Thing and stuff classes in context

Reference 3

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Observation 197c4d1a-34e0-4973-abff-865a2fdd9132 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Emerg- ing properties in self-supervised vision transformers

Reference 4

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Observation 2fb29648-8d04-4c9b-b931-1e7a56e978bd · outbound

This paper cites Learn- ing to generate text-grounded mask for open-world semantic segmentation from only image-text pairs.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Learn- ing to generate text-grounded mask for open-world semantic segmentation from only image-text pairs

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:34.806179Z

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Observation dfd3e05b-4488-4f30-9402-a96375d05028 · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Masked-attention mask transformer for universal image segmentation

Reference 6

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Observation bfa636b9-7eb7-4292-983e-64c112e2cc83 · outbound

This paper cites Reproducible scaling laws for contrastive language-image learning.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Reproducible scaling laws for contrastive language-image learning

Reference 7

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Observation 5272cc8f-b55b-45cd-92b1-ba3b62e3fe37 · outbound

This paper cites CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation

Reference 8

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Observation 5c3e7584-fa90-43a4-8185-0a4fe387b6ed · outbound

This paper cites Open-Vocabulary Universal Image Segmentation with MaskCLIP.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Open-Vocabulary Universal Image Segmentation with MaskCLIP

Reference 9

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Observation d277024b-19b1-4fc2-b1f8-077edea43f61 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 10

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Observation 78f240fe-ec8b-4796-aadf-3b4b65160abe · outbound

This paper cites The pascal visual object classes (voc) challenge.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation The pascal visual object classes (voc) challenge

Reference 11

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Observation 0881ea7d-4ff2-4390-8134-9d8081342a30 · outbound

This paper cites Scaling up visual and vision-language representa- tion learning with noisy text supervision.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:34.636723Z

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Observation 2274c32d-a5ad-4847-8272-79a291cfca52 · outbound

This paper cites Diffusion models for zero-shot open-vocabulary segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Diffusion models for zero-shot open-vocabulary segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:34.594676Z

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Observation 6b827282-485e-49ef-88eb-515e84b63026 · outbound

This paper cites Segment anything.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Segment anything

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:34.561979Z

Source-reported events for the cited work

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Observation 423ec143-ef68-46a0-8dd1-e6d917960360 · outbound

This paper cites ProxyCLIP: Proxy attention improves clip for open-vocabulary segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation ProxyCLIP: Proxy attention improves clip for open-vocabulary segmentation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:34.527327Z

Source-reported events for the cited work

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Observation 6dc9b584-b0b6-4919-87f8-cb622f46ef15 · outbound

This paper cites ClearCLIP: Decom- posing clip representations for dense vision-language infer- ence.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation ClearCLIP: Decom- posing clip representations for dense vision-language infer- ence

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:34.469805Z

Source-reported events for the cited work

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Observation 0e733d73-93f9-4fc4-b743-8420134f2b27 · outbound

This paper cites Anti- adversarially manipulated attributions for weakly and semi- supervised semantic segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Anti- adversarially manipulated attributions for weakly and semi- supervised semantic segmentation

Reference 17

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Observation 9fa5cd2c-22c7-4835-9522-776468f09def · outbound

This paper cites A Closer Look at the Explainability of Contrastive Language-Image Pre-training.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation A Closer Look at the Explainability of Contrastive Language-Image Pre-training

Reference 18

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Observation 29a748cd-ff89-4e14-8ada-6c5fa9bea170 · outbound

This paper cites Open-vocabulary semantic segmentation with mask-adapted clip.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Open-vocabulary semantic segmentation with mask-adapted clip

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:34.308320Z

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Observation cafa38ba-9d28-4b8a-a449-f375d9a8c235 · outbound

This paper cites CLIP is also an efficient segmenter: A text-driven approach for weakly su- pervised semantic segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation CLIP is also an efficient segmenter: A text-driven approach for weakly su- pervised semantic segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:34.274869Z

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Observation 449e98ac-1963-48a5-babc-1bd3c2baeffb · outbound

This paper cites TagCLIP: A local-to-global framework to enhance open-vocabulary multi-label classification of clip without training.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation TagCLIP: A local-to-global framework to enhance open-vocabulary multi-label classification of clip without training

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:34.237480Z

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Observation 9744087a-2167-4d00-b9a8-cbc741bbe050 · outbound

This paper cites Fully convolutional networks for semantic segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Fully convolutional networks for semantic segmentation

Reference 22

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Observation 66449b3c-c91c-4a61-8ca8-18a167b6a519 · outbound

This paper cites SegCLIP: Patch aggregation with learnable centers for open-vocabulary semantic segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation SegCLIP: Patch aggregation with learnable centers for open-vocabulary semantic segmentation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:34.169300Z

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Observation 4cbf94d9-3b2a-4eb4-9e34-5ee0d946aa24 · outbound

This paper cites The role of context for object detection and semantic segmentation in the wild.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation The role of context for object detection and semantic segmentation in the wild

Reference 24

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Observation 82a288b3-01c7-41c6-9ee1-579ecf5ed70b · outbound

This paper cites Learning transferable visual models from natural language supervision.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Learning transferable visual models from natural language supervision

Reference 25

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Observation d3175b13-b7f6-4075-a543-9417b6f3b717 · outbound

This paper cites Per- ceptual grouping in contrastive vision-language models.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Per- ceptual grouping in contrastive vision-language models

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:33.861006Z

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Observation e7ea2c76-8fe9-4204-97ac-b02c22479857 · outbound

This paper cites ViewCo: Discovering text-supervised segmentation masks via multi-view semantic consistency.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation ViewCo: Discovering text-supervised segmentation masks via multi-view semantic consistency

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:33.818391Z

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Observation dc4bb987-e0bd-48da-8858-9c13f054bca6 · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation High-resolution image syn- thesis with latent diffusion models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:33.785884Z

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Observation 8db690b3-cad4-42db-9946-f1bf9512de3a · outbound

This paper cites To- ken contrast for weakly-supervised semantic segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation To- ken contrast for weakly-supervised semantic segmentation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:33.749028Z

Source-reported events for the cited work

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Observation ff6fa4f0-1142-43e1-ac19-a7faab042712 · outbound

This paper cites Laion-5B: An open large-scale dataset for training next generation image-text models.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Laion-5B: An open large-scale dataset for training next generation image-text models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:33.713015Z

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Observation 3ef15bd6-675a-46e3-ae5b-d66e8b41b6db · outbound

This paper cites ReCo: Re- trieve and co-segment for zero-shot transfer.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation ReCo: Re- trieve and co-segment for zero-shot transfer

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:33.672270Z

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Observation 76af3956-dcb7-43e3-82b4-dade04414c75 · outbound

This paper cites iSeg: An Iterative Refinement-based Framework for Training-free Segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation iSeg: An Iterative Refinement-based Framework for Training-free Segmentation

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:53:32.128129Z

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Observation ee87535e-4d4b-41ad-8387-f8c20bdb7c73 · outbound

This paper cites CLIP as RNN: Segment countless visual concepts with- out training endeavor.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation CLIP as RNN: Segment countless visual concepts with- out training endeavor

Reference 33

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Observation 0e62b1ef-f7ee-410a-9bee-51decc852a1f · outbound

This paper cites Sclip: Rethinking self-attention for dense vision-language inference.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Sclip: Rethinking self-attention for dense vision-language inference

Reference 34

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Observation 537c9fe7-9180-47fc-beb5-44f643700f30 · outbound

This paper cites Sam-clip: Merging vision foundation models towards semantic and spatial understanding.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Sam-clip: Merging vision foundation models towards semantic and spatial understanding

Reference 35

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Observation f1e187ce-2dfd-4150-8125-3768a669df13 · outbound

This paper cites Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T15:53:31.546345Z

Source-reported events for the cited work

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Observation e021f2c3-832d-4647-886f-e9733f0fac57 · outbound

This paper cites Clip-dinoiser: Teaching clip a few dino tricks for open- vocabulary semantic segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Clip-dinoiser: Teaching clip a few dino tricks for open- vocabulary semantic segmentation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:33.361545Z

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Observation 5268430a-a04e-4686-bde8-0acc42f79d99 · outbound

This paper cites From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:53:32.041705Z

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Observation 0fd812d0-79ab-49e5-8126-9f64443fd613 · outbound

This paper cites Sed: A simple encoder-decoder for open- vocabulary semantic segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Sed: A simple encoder-decoder for open- vocabulary semantic segmentation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:33.203098Z

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Observation 6818b0d7-7fbb-4083-b73b-4807879449f2 · outbound

This paper cites Alvarez, and Ping Luo.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Alvarez, and Ping Luo

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:33.169597Z

Source-reported events for the cited work

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Observation 82e486ea-48b1-475c-bb02-2c9994ba1305 · outbound

This paper cites Clims: Cross language image matching for weakly supervised se- mantic segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Clims: Cross language image matching for weakly supervised se- mantic segmentation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:32.990468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 179b678d-4304-41da-b23a-b38123aa2586 · outbound

This paper cites Rewrite caption semantics: Bridging seman- tic gaps for language-supervised semantic segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Rewrite caption semantics: Bridging seman- tic gaps for language-supervised semantic segmentation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:32.915948Z

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Observation aa91ddfa-5545-4106-83cb-1dbe4aaf9647 · outbound

This paper cites Groupvit: Semantic segmentation emerges from text supervision.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Groupvit: Semantic segmentation emerges from text supervision

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:32.850323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 34810fe9-a51d-4920-83db-c970a2641733 · outbound

This paper cites Learning open-vocabulary seman- tic segmentation models from natural language supervision.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Learning open-vocabulary seman- tic segmentation models from natural language supervision

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:32.809151Z

Source-reported events for the cited work

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Observation 2f00c189-f6a3-4241-a794-bda1d061773d · outbound

This paper cites Open-vocabulary panoptic segmentation with text-to-image diffusion models.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Open-vocabulary panoptic segmentation with text-to-image diffusion models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:32.773430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 16223171-313d-4031-a611-a09d431217d6 · outbound

This paper cites Multi-class token transformer for weakly supervised se- mantic segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Multi-class token transformer for weakly supervised se- mantic segmentation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:32.726561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0732b187-27e6-4bd9-9ecd-79a5528df144 · outbound

This paper cites Side adapter network for open-vocabulary semantic segmentation.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Side adapter network for open-vocabulary semantic segmentation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:32.660574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dedbb98a-65d2-424e-b811-32470ae6b714 · outbound

This paper cites Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T15:53:31.853023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91ee6cf4-fc84-47f8-bc5f-272acd54846b · outbound

This paper cites Open vocabulary scene parsing.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Open vocabulary scene parsing

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:32.616043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c5655f20-8b1e-4db0-8adb-12423fbf6252 · outbound

This paper cites Semantic under- standing of scenes through the ade20k dataset.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Semantic under- standing of scenes through the ade20k dataset

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-12T15:53:31.878658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3241dc5e-106a-4fec-82df-4def85422460 · outbound

This paper cites Extract free dense labels from clip.

CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation Extract free dense labels from clip

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:53:32.461065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation a7d5046b-d83a-4772-9b7f-346ca2037fe4 · inbound

CorrCLIP: Reconstructing Patch Correlations in CLIP for Open-Vocabulary Semantic Segmentation cites this paper.

CorrCLIP: Reconstructing Patch Correlations in CLIP for Open-Vocabulary Semantic Segmentation CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-12T20:04:44.106636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fee426a-3576-4ef6-b5f8-473170d116dc · inbound

Perception Encoder: The best visual embeddings are not at the output of the network cites this paper.

Perception Encoder: The best visual embeddings are not at the output of the network CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation

Reference 128

Resolution: verified exact; arxiv_id, observed 2026-05-13T22:21:15.798787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.